Approximately 2.2 million Americans and 4.5 million Europeans have atrial fibrillation, and this infirmity is expected to increase proportionally with an aging population. Between 1990 to 2005, out of 270,000 inpatient records showing a diagnosis of atrial fibrillation from the National Hospital Discharge Survey, there were 1,144 individuals who were treated with contact ablation, with an increasing percentage of treatment towards 2005. Assuming the 1990-2005 trend is maintained, this would suggest that roughly 2.0% of the hospitalized atrial fibrillation sufferers would be treated via atrial ablation by 2015. Further assuming that 10% of current atrial fibrillation sufferers in the US are hospitalized by 2015, this suggests that ˜4,400 individuals will be treated with atrial ablation within this time period.
Diagnosed atrial fibrillation is simplistically categorized as a function of the duration and frequency of atrial fibrillation episodes. Generally, atrial fibrillation episodes that persist for <30 s are not categorized. If there is but one (i.e., a first) diagnosed episode of atrial fibrillation, the atrial fibrillation is categorized as “first detected atrial fibrillation.” If subsequent episode of atrial fibrillation occur each with durations <7 days, the condition generally is termed “paroxysmal atrial fibrillation.” Episodes that recur with durations >7 days generally are termed “persistent atrial fibrillation.” The atrial fibrillation condition that is characterized by long term ongoing episodes of atrial fibrillation is generally termed “permanent atrial fibrillation.”
Contact atrial electrograms recorded during atrial fibrillation (AF) are characterized by rapid deflections with changing amplitudes, cycle lengths (CLs), and morphologies. Unlike other atrial tachyarrhythmias with regular activation patterns, atrial fibrillation has complex activation patterns that make elucidation of atrial fibrillation mechanisms difficult. Mapping of atrial cycle length or atrial activation rates has been proposed in the art as an alternative to mapping activation sequences.
It has been hypothesized that sites with the fastest activation rates represent the locations of the drivers of atrial fibrillation (focal or reentrant) and could be potential ablation targets. Some support for this contention has been found in both, clinical and experimental studies. The measurements of atrial fibrillation activations rates can also be used to track drug effects, autonomic manipulations, and ablation response.
One of the principal limitations of the activation rate mapping approach is the technical difficulty encountered in obtaining reliable measurements. The complexity of atrial fibrillation electrograms renders detection of deflections and the calculation of the cycle lengths and activation rates difficult in both, the time and the frequency domains. Deflection-to-deflection intervals can be measured manually with the use of calipers, but because of the variability of atrial fibrillation cycle lengths, an average of several intervals is needed to characterize the atrial fibrillation cycle length. This is an arduous task for the technician making the measurements.
An alternative method that has been employed in the art is a manual setting of an amplitude or slope threshold value that can be used to detect deflections. The principal limitation of this manual threshold approach is the subjectivity required on the part of the technician to distinguish noise from atrial activation. In addition, automatic algorithms that detect deflections based on fixed threshold levels are prone to oversensing or undersensing.
Dominant frequency (DF) analysis is yet another known approach, and uses the frequency that contains the most power as the estimate of the activation rate. Dominant frequency analysis works well in the estimation of activation rates if the atrial fibrillation electrograms have a certain amount of regularity, but the correlation is reduced with highly irregular waveforms and complex morphologies. Such irregularity of the waveforms and signal complexity renders dominant frequency analysis inaccurate.
There is a need for a robust algorithm that produces accurate results that can be validated and withstand rigorous testing. There is also a need for a system that produces a more accurate and robust methodology of locating optimal sites for ablation in patients suffering from atrial fibrillations.